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EnSa-EAE: A New EEG-Based Framework for Human Cognitive Workload Recognition
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AI
DOI：10.1080/10447318.2026.2625965.png)
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Abstract
En 中文
Cognitive workload (CW) recognition is crucial in real-time and dynamic decision-making environments. Electroencephalogram (EEG) provides a cost-effective, flexible, and high-resolution approach for CW recognition.Various research studies has been developed that faces less accuracy in prediction and also leads error in estimation. Hence, a robust deep learning (DL) model for CW estimation is proposed. Initially, EEG signals from EEG workload dataset are pre-processed then third-order spectral cumulant features are extracted using residual dilated bidirectional long short-term memory network for obtaining higher-level feature representation. Subsequently, temporal and spectral features are fused using an attention mechanism, and an ensemble spatial attention with an enhanced stack autoencoder is employed for CW recognition. Proposed method attained an accuracy of 95.1% in no-task experiment and 95.5% in simultaneous capacity-based multitasking activity. Experimental findings demonstrate the effectiveness of proposed method in EW prediction. Proposed model has the potential to enhance decision-making and performances in constraint environment.
Keywords:
Cognitive workload
deep learning
enhanced stack autoencoder
Bi-LSTM
temporal convolutional autoencoder
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